DREEAM: Guiding attention with evidence for improving document-level relation extraction

Y Ma, A Wang, N Okazaki - arXiv preprint arXiv:2302.08675, 2023 - arxiv.org
Document-level relation extraction (DocRE) is the task of identifying all relations between
each entity pair in a document. Evidence, defined as sentences containing clues for the …

A novel table-to-graph generation approach for document-level joint entity and relation extraction

R Zhang, Y Li, L Zou - Proceedings of the 61st Annual Meeting of …, 2023 - aclanthology.org
Document-level relation extraction (DocRE) aims to extract relations among entities within a
document, which is crucial for applications like knowledge graph construction. Existing …

Semi-automatic data enhancement for document-level relation extraction with distant supervision from large language models

J Li, Z Jia, Z Zheng - arXiv preprint arXiv:2311.07314, 2023 - arxiv.org
Document-level Relation Extraction (DocRE), which aims to extract relations from a long
context, is a critical challenge in achieving fine-grained structural comprehension and …

Consistency guided knowledge retrieval and denoising in llms for zero-shot document-level relation triplet extraction

Q Sun, K Huang, X Yang, R Tong, K Zhang… - Proceedings of the ACM …, 2024 - dl.acm.org
Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in information
systems that aims to simultaneously extract entities with semantic relations from a document …

Revisiting document-level relation extraction with context-guided link prediction

M Jain, R Mutharaju, R Kavuluru, K Singh - Proceedings of the AAAI …, 2024 - ojs.aaai.org
Document-level relation extraction (DocRE) poses the challenge of identifying relationships
between entities within a document. Existing approaches rely on logical reasoning or …

Shadowfax: Harnessing textual knowledge base population

M Prieur, C Du Mouza, G Gadek… - Proceedings of the 47th …, 2024 - dl.acm.org
Knowledge base population (KBP) from texts involves the extraction and organization of
information from unstructured textual data to enhance or create a structured knowledge …

A unified positive-unlabeled learning framework for document-level relation extraction with different levels of labeling

Y Wang, X Liu, W Hu, T Zhang - arXiv preprint arXiv:2210.08709, 2022 - arxiv.org
Document-level relation extraction (RE) aims to identify relations between entities across
multiple sentences. Most previous methods focused on document-level RE under full …

Autore: Document-level relation extraction with large language models

L Xue, D Zhang, Y Dong, J Tang - arXiv preprint arXiv:2403.14888, 2024 - arxiv.org
Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending
and generating text, motivating numerous researchers to utilize them for Information …

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

S Meng, X Hu, A Liu, S Li, F Ma, Y Yang… - arXiv preprint arXiv …, 2023 - arxiv.org
How to identify semantic relations among entities in a document when only a few labeled
documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial …

A dataset for hyper-relational extraction and a cube-filling approach

YK Chia, L Bing, SM Aljunied, L Si, S Poria - arXiv preprint arXiv …, 2022 - arxiv.org
Relation extraction has the potential for large-scale knowledge graph construction, but
current methods do not consider the qualifier attributes for each relation triplet, such as time …